Concentration detection methods can be used in various applications such as in the diagnosis of neuro-cognitive conditions, for example, the Attention Deficit or Hyperactivity Disorder (ADHD). In addition, they can be used for performance monitoring and enhancement in sports, gaming, driving etc. or for assessing work related stress. Concentration detection methods can also be used to monitor the effectiveness of medication such as in clinical drug trials or the effectiveness of therapy and rehabilitation such as biofeedback.
In general, it is preferable that a concentration detection method allows a continuous detection and measurement of the concentration or attention levels. Furthermore, a concentration detection method needs to be accurate and robust. It is also preferable for the concentration detection method to be easily used and to be of a low cost.
Monastra and Lubar [Monastra and Lubar, 2000—U.S. Pat. No. 6,097,980—Quantitative electroencephalographic (QEEG) process and apparatus for assessing attention deficit hyperactivity disorder; V. J. Monastra, S. Lynn, M. Linden, J. F. Lubar, J. Gruzelier, and T. J. LaVaque, “Electroencephalographic Biofeedback in the Treatment of Attention-Deficit/Hyperactivity Disorder,” Applied Psychophysiology and Biofeedback, vol. 30, no. 2, pp. 95-114, June 2005.] described a method to calculate an attention index for concentration detection. This attention index is calculated as the average of the theta over beta power ratio for each of the following tasks to be performed by the subject. In these tasks, the subject has to keep his or her eyes open with a fixed gaze (used as the baseline), read, listen or draw. The calculation of the attention index is shown in Equation (1) whereby EEGpowerthetaTask is the theta power, EEGpowerbetaTask is the beta power and N is the total number of tasks performed. The theta band is defined as 4-8 Hz whereas the beta band is defined as 13-21 Hz.
                              Attention          ⁢                                          ⁢          Index                =                              1            N                    ⁢                                    ∑                              Task                =                1                            N                        ⁢                                                  ⁢                                          EEGpower                theta                Task                                            EEGpower                beta                Task                                                                        (        1        )            
FIG. 1 shows graphs illustrating the basis for development of another prior art Cox et al [Cox et al, 2004—US20040152995A1—Method, apparatus, and computer program product for assessment of attentional impairments]. FIGS. 1A and 1B are graphical representations of the EEG frequency dimension, illustrating the EEG power spectrum for two cognitive tasks for a consistent EEG transition case and an inconsistent EEG transition case respectively. In each of the FIGS. 1A and 1B, curves 102A and 102B represent the power spectrum of a subject performing a task and curves 104A and 104B represent the power spectrum of the same subject while performing an adjacent task. In FIG. 1A, curve 102A is above curve 104A at lower frequencies and mostly below curve 104A at higher frequencies (above 16 Hz). This shows that a shift from one task to another (from curve 102A to 104A) results in an increase of higher frequencies and a decrease of lower frequencies. In contrast, in FIG. 1B, no specific change in the frequency distribution over the two tasks is observed.
The EEG consistency shown in FIG. 1 is used as a basis for development of Cox et al. With this basis, Cox et al described two measures for the assessment of attentional impairments. The first measure is the Consistency index (CI) calculated as the EEG power change distance (PCD) transition from one task to another as shown in Equation (2). In Equation (2), N represents the total number of tasks and 8, represents whether the PCD is above (δi=1), equal to (δi=0) or below (δi=−1) a cutoff value.
      ∑    belowcutoff    ⁢          ⁢      δ    i  represents the sum of δi below the cutoff value and
      ∑    abovecutoff    ⁢          ⁢      δ    i  represents the sum of δi above the cutoff value.
                    CI        =                  100          ⁢                                                                1                N                            ⁢                              (                                                                            ∑                      belowcutoff                                        ⁢                                                                                  ⁢                                          δ                      i                                                        -                                                            ∑                      abovecutoff                                        ⁢                                                                                  ⁢                                          δ                      i                                                                      )                                                                                    (        2        )            
The second measure in Cox et al is the Alpha Blockade Index (ABI) which is based on the spectral analysis, particularly of the alpha activity in the brain. The calculation of the ABI is given in Equation (3). In Equation 3, αi represents the alpha power in the subject's brain during the ith task or the ith resting period and k represents the total number of tasks and resting periods.
                    ABI        =                              100                          k              -              1                                ⁢                                    ∑                              i                =                2                            k                        ⁢                                                  ⁢                                                                                              α                    i                                    -                                      α                                          i                      -                      1                                                                                        max                  ⁡                                      (                                                                  α                                                  i                          -                          1                                                                    ,                                              α                        i                                                              )                                                                                                                        (        3        )            
Cowan and Prell [Cowan and Prell, 1999—U.S. Pat. No. 5,983,129—Method for determining an individual's intensity of focused attention and integrating same into computer program] proposed to use EEGs collected from the frontal lobe of the subject's brains and defined an Attention Indicator that is inversely proportional to a mathematical transformation of an amplitude measure of the frontal lobe EEG. The frontal lobe EEG is within the frequency band of 0-11 Hz. However, since the amplitude of the EEG changes over time and varies significantly across different subjects, the method in Cowan and Prell is unable to provide a quantifiable level of attention.
Other prior arts for implementing concentration detection methods are as follows: E. Molteni, A. M. Bianchi, M. Butti, G. Reni, C. Zucca, “Analysis of the dynamical behaviour of the EEG rhythms during a test of sustained attention” Proceeding of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. EMBS 2007), Aug. 22-26, 2007, pp. 1298-1301; C. A. Mann, J. F. Lubar, A. W. Zimmerman, C. A. Miller, and R. A. Muenchen, “Quantitative analysis of EEG in boys with attention deficit-hyperactivity disorder: Controlled study with clinical implications,” Pediatric Neurology, vol. 8, no. 1, pp. 30-36, January-February 1992.; A. J. Haufler, T. W. Spalding, D. L. Santa Maria, and B. D. Hatfield, “Neuro-cognitive activity during a self-paced visuospatial task: comparative EEG profiles in marksmen and novice shooters,” Biological Psychology, vol. 53, no. 2-3, pp. 131-160, July 2000.; T.-P. Jung, S. Makeig, M. Stensmo, and T. J. Sejnowski, “Estimating alertness from the EEG power spectrum,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 1, pp. 60-69, 1997.
None of the prior art methods can provide quantifiable measures, for example 1-100 marks, for the level of attention detected. In addition, the prior art methods were based on spectral analysis and are hence inherently sensitive to all kinds of variations, for example, variations due to artefacts, noises, measurement devices, etc. The prior art methods are also unable to provide a consistent measure across different subjects.
FIG. 2 shows a flowchart 200 illustrating the general process of concentration detection methods in the prior arts based on spectral analysis. As shown in FIG. 2, in the prior arts, a frequency analysis step 202 is performed on the acquired EEG. Next, an Index is generated in step 204 to give an Attention indicator for concentration detection.
Hence, in view of the above, there exists a need for a method and system for concentration detection which seek to address at least one of the above problems.